Today, most of the data in business applications is stored in relational databases. Relational database systems are so popular, because they offer solutions to many problems around data storage, such as efficiency, effectiveness, usability, security and multi-user support. To benefit from these...

Today, most of the data in business applications is stored in relational database systems or in data warehouses built on top of relational database systems. Often, for more data is available than can be processed by standard learning algorithms in reasonable time. This paper presents an...

Support Vector Machines (SVMs) have become a popular tool for learning with large amounts of high dimensional data. However, it may sometimes be preferable to learn incrementally from previousSVM results, as computing a SVM is very costly in terms of time and memory consumption or because the...

Time series analysis is an important and complex problem in machine learning and statistics. Real-world applications can consist of very large and high dimensional time series data. Support Vector Machines (SVMs) are a popular tool for the analysis of such data sets. This paper presents some SVM...

This paper investigates the use of Design of Experiments in observational studies in order to select informative observations and features for classification. D-optimal plans are searched for in existing data and based on these plans the variables most relevant for classification are determined....

This paper describes an approach for selecting instances in regression problems in the cases where observations x are readily available, but obtaining labels y is hard. Given a database of observations, an algorithm inspired by statistical design of experiments and kernel methods is presented...

A workbench for knowledge acquisition and data analysis is presented and its use for the classification of business cycles is investigated. Inductive Logic Programming (ILP) allows to model relations between intervals, e.g. time or value intervals. Moreover, the user of the workbench is...

The analysis of temporal data is an important issue of current research, because most real-world data either explicitly or implicitly contains some information about time. The key to successfully solving temporal learning tasks is to analyze the assumptions that can be made and prior knowledge...